Kick the Tires on Your Interest-Rate Risk Model

Consider adopting a ‘maintenance schedule’ for model assumptions.

July 31, 2012

If you’ve ever owned a car, you probably know about maintenance schedules. That involves reviewing and replacing items such as oil, spark plugs, and tires periodically to ensure the vehicle continues to run properly.

Just like an automobile, credit unions should review their IRR model every so often to determine whether current behavior assumptions are appropriate and reasonable.

When opening the hood of their IRR model, credit unions should review the following five assumptions.

1. Interest-rate scenarios to be modeled

While examiners expect +400 basis point (bp) rate scenarios for earnings simulations, they also expect to see nonparallel rate moves as well. This is where the short- and long-term rates of the yield curve move by different magnitudes.

Historical yield curve analysis will help assist with selection of the most realistic rate change scenarios.

2. Reinvestment and discount rates

The earnings-at-risk simulation relies heavily on reinvestment rates and other repricing rates to calculate the changes in interest income and expense.

Discount rates, on the other hand, are used more specifically to determine the present value of future cash flows used more for long-term fair value analysis (economic value of equity or net economic value).

Management should use current offering and other market rates to consistently adjust these, especially after recent rate changes and new products.

3. Rate sensitivities and time lags

Sensitivities, sometimes known as “betas,” are numbers that help describe the pricing relationship of a particular account in response to movement in market rates.

Time lags specify how much time would pass before the account will begin to experience a rate change. For example, an account with a rate sensitivity of 30% and lag of three would imply that if market rates increased 100 bp, the account would increase by only 30 bp three months after the initial market rate move.

Management should spend time reviewing historical rate performance to confirm that current sensitivity and lags assumptions are reasonable for their current activities.

4. Average lives of nonmaturing liabilities

Widely considered the most mystifying assumptions for the model, the question here is what kind of “maturities” do the balances of your nonmaturing liabilities have?

Financial institutions should perform an analysis of nonmaturing liability behavior and identify those funds which can be considered volatile versus core funding.

Once separated, more volatile balances should be assigned shorter average lives versus core funding, which typically carries a longer average life.

5. Asset prepayment/liability decay

Asset prepayments and liability decay rates are important to capture optionality on certain accounts. For assets, loans and mortgage-related securities have the ability to prepay principal.

On the liability side, nonmaturing liabilities incorporate decay rates to simulate the effect of depositors withdrawing balances in certain interest rate environments. A higher prepayment or decay rate would usually imply a shorter average life of the particular account.

While this list certainly isn’t exhaustive, these assumptions could be considered the most critical and impactful to your reporting for both earnings at risk and long-term fair value analysis.

One approach credit unions take is to routinely “stress test” the assumptions in the model. Management may find this helpful as it identifies which items have the most significant impact on model results.

An example of stressing an assumption would be removing time lags on nonmaturing liabilities and doubling rate sensitivities. This adjustment would highlight the potential increase in interest expense if the institution would have to be more competitive from a pricing standpoint for those particular products.

Because an assumption is, by definition, a thing that is accepted as true without proof, there isn’t necessarily a right or wrong way to model them. Also, while back testing is necessary and history may be insightful, it can’t predict the future.

Prudent risk managers are aware of these limitations and will strive to maintain reasonable and supportable modeling assumptions. They will also consistently tweak and adjust their assumptions to better understand the implications those assumptions have on their model output and, ultimately, asset/liability management strategy.